Identifying changes in the reproduction number, rate of spread, and doubling time during the course of the COVID-19 outbreak whilst accounting for potential biases due to delays in case reporting both nationally and subnationally.
Figure 1: The results of the latest reproduction number estimates (based on estimated cases with a date of infection on the 2020-03-22) can be summarised by whether cases are likely increasing or decreasing. This represents the strength of the evidence that the reproduction number in each region is greater than or less than 1, respectively. Countries are only included if at least 100 cases have been reported on a single day. Countries with fewer than 100 cases reported on a single day are not included in the analysis (light grey).
Figure 1: Cases with date of infection on the 2020-03-22 and the time-varying estimate of the effective reproduction number (light bar = 90% credible interval; dark bar = the 50% credible interval.). Regions are ordered by the number of expected daily cases and shaded based on the expected change in daily cases. The dotted line indicates the target value of 1 for the effective reproduction no. required for control and a single case required for elimination.
Figure 2: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.
Figure 3: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in the regions expected to have the highest number of incident cases. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.
Figure 4: Time-varying estimate of the effective reproduction number (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence. The dotted line indicates the target value of 1 for the effective reproduction no. required for control.
Figure 5: Cases by date of report (bars) and their estimated date of infection (light ribbon = 90% credible interval; dark ribbon = the 50% credible interval) in all regions. Estimates are shown up to the 2020-03-22. Confidence in the estimated values is indicated by translucency with increased translucency corresponding to reduced confidence.
| Country/Region | New infections | Expected change in daily cases | Effective reproduction no. | Doubling time (days) |
|---|---|---|---|---|
| Algeria | 85 (21 – 152) | Likely increasing | 1.6 (0.8 – 2.3) | 8.7 (3.3 – Cases decreasing) |
| Argentina | 131 (43 – 203) | Likely increasing | 1.4 (0.9 – 1.9) | 11 (4.3 – Cases decreasing) |
| Australia | 429 (246 – 635) | Unsure | 1.1 (0.9 – 1.4) | 46 (9.3 – Cases decreasing) |
| Austria | 833 (445 – 1209) | Unsure | 1.1 (0.8 – 1.4) | 43 (8.3 – Cases decreasing) |
| Belgium | 1789 (931 – 2672) | Increasing | 1.6 (1 – 2.2) | 6.9 (3.6 – 55) |
| Brazil | 538 (253 – 811) | Likely increasing | 1.3 (0.8 – 1.8) | 15 (5.4 – Cases decreasing) |
| Canada | 1037 (546 – 1530) | Increasing | 1.4 (1 – 1.9) | 8.4 (4.3 – 430) |
| Chile | 363 (166 – 568) | Likely increasing | 1.5 (0.9 – 2.1) | 8.1 (3.8 – Cases decreasing) |
| China | 110 (62 – 151) | Unsure | 1.1 (0.8 – 1.5) | 37 (8.6 – Cases decreasing) |
| Czech Republic | 307 (137 – 454) | Likely increasing | 1.3 (0.8 – 1.7) | 13 (4.6 – Cases decreasing) |
| Denmark | 224 (98 – 331) | Likely increasing | 1.4 (0.9 – 2) | 9.1 (4.2 – Cases decreasing) |
| Dominican Republic | 168 (32 – 285) | Likely increasing | 1.6 (0.7 – 2.4) | 8.6 (3.3 – Cases decreasing) |
| Ecuador | 178 (76 – 272) | Unsure | 1.1 (0.7 – 1.5) | 1300 (8.3 – Cases decreasing) |
| Estonia | 68 (22 – 104) | Likely increasing | 1.4 (0.7 – 1.9) | 10 (3.6 – Cases decreasing) |
| Finland | 120 (21 – 197) | Unsure | 1.2 (0.7 – 1.7) | 26 (5.3 – Cases decreasing) |
| France | 4644 (2593 – 6943) | Likely increasing | 1.3 (1 – 1.8) | 13 (5.7 – Cases decreasing) |
| Germany | 5764 (3253 – 8030) | Likely increasing | 1.2 (0.9 – 1.6) | 18 (6.7 – Cases decreasing) |
| Greece | 104 (44 – 163) | Likely increasing | 1.3 (0.8 – 1.8) | 16 (5.3 – Cases decreasing) |
| India | 167 (82 – 270) | Likely increasing | 1.5 (0.9 – 2) | 9.8 (4.4 – Cases decreasing) |
| Indonesia | 183 (69 – 288) | Likely increasing | 1.4 (0.8 – 2) | 12 (4.3 – Cases decreasing) |
| Iran | 3556 (1759 – 5118) | Likely increasing | 1.4 (0.9 – 1.9) | 9 (4.5 – Cases decreasing) |
| Ireland | 353 (138 – 517) | Likely increasing | 1.4 (0.9 – 1.8) | 12 (4.8 – Cases decreasing) |
| Israel | 567 (208 – 826) | Likely increasing | 1.3 (0.8 – 1.9) | 13 (4.9 – Cases decreasing) |
| Italy | 6295 (3549 – 9060) | Unsure | 1.1 (0.8 – 1.4) | 65 (9.3 – Cases decreasing) |
| Japan | 164 (87 – 230) | Increasing | 1.5 (1 – 2) | 7.6 (4.1 – 57) |
| Luxembourg | 177 (46 – 286) | Unsure | 1.1 (0.6 – 1.5) | 1400 (6.7 – Cases decreasing) |
| Malaysia | 187 (103 – 268) | Unsure | 1.1 (0.8 – 1.4) | 42 (8.5 – Cases decreasing) |
| Mexico | 180 (68 – 283) | Increasing | 1.6 (0.9 – 2.3) | 6.8 (3.3 – Cases decreasing) |
| Morocco | 108 (24 – 180) | Increasing | 1.8 (1 – 2.6) | 6 (2.8 – Cases decreasing) |
| Netherlands | 1340 (699 – 1930) | Likely increasing | 1.4 (0.9 – 1.8) | 11 (5 – Cases decreasing) |
| Norway | 313 (156 – 479) | Unsure | 1.2 (0.7 – 1.5) | 28 (6.4 – Cases decreasing) |
| Pakistan | 167 (52 – 301) | Unsure | 1.3 (0.7 – 1.9) | 19 (4.7 – Cases decreasing) |
| Panama | 149 (47 – 237) | Likely increasing | 1.4 (0.8 – 1.9) | 11 (4.1 – Cases decreasing) |
| Peru | 138 (35 – 231) | Likely increasing | 1.6 (0.7 – 2.3) | 7.7 (3.2 – Cases decreasing) |
| Philippines | 496 (125 – 835) | Increasing | 2.1 (0.9 – 3.3) | 4.2 (2.2 – 42) |
| Poland | 282 (116 – 439) | Likely increasing | 1.5 (0.9 – 2.1) | 9 (4.1 – Cases decreasing) |
| Portugal | 904 (390 – 1385) | Likely increasing | 1.4 (0.9 – 2) | 9.2 (4.2 – Cases decreasing) |
| Qatar | 55 (16 – 96) | Likely increasing | 1.9 (0.8 – 2.9) | 5.3 (2.6 – Cases decreasing) |
| Romania | 301 (132 – 465) | Likely increasing | 1.6 (1 – 2.2) | 7.8 (3.8 – Cases decreasing) |
| Russia | 356 (128 – 557) | Increasing | 1.8 (0.9 – 2.6) | 5.6 (2.9 – 81) |
| Saudi Arabia | 160 (49 – 265) | Unsure | 1.2 (0.8 – 1.6) | 29 (5.9 – Cases decreasing) |
| Serbia | 166 (44 – 281) | Likely increasing | 2 (0.8 – 3.1) | 4.8 (2.3 – Cases decreasing) |
| Singapore | 84 (30 – 124) | Likely increasing | 1.4 (0.9 – 1.9) | 10 (4.3 – Cases decreasing) |
| South Africa | 132 (54 – 216) | Unsure | 1.1 (0.6 – 1.6) | 55 (5.2 – Cases decreasing) |
| South Korea | 126 (66 – 181) | Unsure | 1.1 (0.8 – 1.5) | 26 (7.4 – Cases decreasing) |
| Spain | 8770 (5252 – 12784) | Likely increasing | 1.2 (0.9 – 1.6) | 16 (6 – Cases decreasing) |
| Sweden | 389 (192 – 594) | Likely increasing | 1.4 (0.9 – 1.9) | 10 (4.5 – Cases decreasing) |
| Switzerland | 1448 (749 – 2188) | Likely increasing | 1.3 (0.8 – 1.7) | 19 (6.4 – Cases decreasing) |
| Thailand | 182 (64 – 286) | Likely increasing | 1.3 (0.8 – 1.9) | 21 (5.2 – Cases decreasing) |
| Turkey | 3068 (780 – 4827) | Likely increasing | 2.1 (0.9 – 3.1) | 4.3 (2.2 – 45) |
| Ukraine | 135 (25 – 228) | Likely increasing | 2.2 (0.7 – 3.5) | 4.3 (2.2 – Cases decreasing) |
| United Arab Emirates | 91 (22 – 162) | Likely increasing | 1.6 (0.9 – 2.2) | 8.1 (3.1 – Cases decreasing) |
| United Kingdom | 3137 (1707 – 4526) | Increasing | 1.5 (1 – 2.1) | 7.7 (4 – 54) |
| United States of America | 22748 (13251 – 32240) | Increasing | 1.4 (1 – 1.8) | 9.2 (4.8 – 100) |
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